利用生成式预训练重建基于AI的放射影像解读中的患者特异性混杂因素
Reconstruction of Patient-Specific Confounders in AI-based Radiologic Image Interpretation using Generative Pretraining
AI总结：
本文提出自条件扩散模型DiffChest，通过大规模胸片生成式预训练在患者层面可视化诊断混杂因素，并验证其识别一致性与诊断准确性。
摘要：
在自动化诊断辅助系统（例如由人工智能驱动的系统）中检测误导性模式，对于确保其可靠性至关重要，尤其是在医疗保健领域。当前用于评估深度学习模型的技术无法在诊断层面将混杂因素可视化。在此，我们提出一种称为DiffChest的自条件扩散模型，并在来自美国和欧洲多个医疗中心、涵盖194,956名患者的515,704张胸部X光片数据集上对其进行训练。DiffChest在患者特异性层面解释分类结果，并将可能误导模型的混杂因素可视化。我们发现，在评估DiffChest识别治疗相关混杂因素的能力时，阅片者之间具有高度一致性，在大多数影像学发现中Fleiss' Kappa值达到0.8或更高。混杂因素以11.1%至100%的患病率被准确捕捉。此外，我们的预训练过程优化了模型，使其从输入X光片中捕捉最相关的信息。在诊断胸腔积液和心功能不全等11种胸部疾病时，DiffChest取得了优异的诊断准确性，而对其余疾病也至少达到了足够的诊断准确性。我们的发现凸显了基于扩散模型的预训练在医学图像分类中的潜力，特别是在提供关于混杂因素和模型鲁棒性的洞察方面。
英文摘要：
Detecting misleading patterns in automated diagnostic assistance systems, such as those powered by Artificial Intelligence, is critical to ensuring their reliability, particularly in healthcare. Current techniques for evaluating deep learning models cannot visualize confounding factors at a diagnostic level. Here, we propose a self-conditioned diffusion model termed DiffChest and train it on a dataset of 515,704 chest radiographs from 194,956 patients from multiple healthcare centers in the United States and Europe. DiffChest explains classifications on a patient-specific level and visualizes the confounding factors that may mislead the model. We found high inter-reader agreement when evaluating DiffChest's capability to identify treatment-related confounders, with Fleiss' Kappa values of 0.8 or higher across most imaging findings. Confounders were accurately captured with 11.1% to 100% prevalence rates. Furthermore, our pretraining process optimized the model to capture the most relevant information from the input radiographs. DiffChest achieved excellent diagnostic accuracy when diagnosing 11 chest conditions, such as pleural effusion and cardiac insufficiency, and at least sufficient diagnostic accuracy for the remaining conditions. Our findings highlight the potential of pretraining based on diffusion models in medical image classification, specifically in providing insights into confounding factors and model robustness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
